Simulated LSST Survey of Rr Lyrae Stars Throughout the Local Group

Abstract

We report on a study to determine the efficiency of the Large Synoptic Survey Telescope (LSST) to recover the periods, brightnesses, and shapes of RR Lyrae stars' light curves in the volume extending to heliocentric distances of 1.5 Mpc. We place the smoothed light curves of 30 type ab and 10 type c RR Lyrae stars in 1007 fields across the sky, each of which represents a different realization of the LSST sampling cadences, and that sample five particular observing modes. A light curve simulation tool was used to sample the idealized RR Lyrae stars' light curves, returning each as it would have been observed by LSST, including realistic photometric scatter, limiting magnitudes, and telescope downtime. We report here the period, brightness, and light curve shape recovery as a function of apparent magnitude and for survey lengths varying from 1 to 10 years. We find that 10 years of LSST data are sufficient to recover the pulsation periods with a fractional precision of ∼10 −5 for 90% of ab stars within ≈360 kpc of the Sun in Universal Cadence fields and out to ≈760 kpc for Deep Drilling fields. The 50% completeness level extends to ≈600 kpc and ≈1.0 Mpc for the same fields, respectively. For virtually all stars that had their periods recovered, their light curve shape parameter φ 31 was recovered with sufficient precision to also recover photometric metallicities to within 0.14 dex (the systematic error in the photometric relations). With RR Lyrae stars' periods and metallicities well measured to these distances, LSST will be able to search for halo streams and dwarf satellite galaxies over half of the Local Group, informing galaxy formation models and providing essential data for mapping the Galactic potential. This study also informs the LSST science operations plan for optimizing observing strategies to achieve particular science goals. We additionally present a new [Fe/H]-φ 31 photometric relation in the r band and a new and generally useful metric for defining period recovery for time domain surveys.

Where are the most ancient stars in the Milky Way?

“…State-of-the-art surveys searching for extremely metal-poor stars can photometrically identify stars with [Fe/H] < −3 with a success rate of up to ∼25%; the majority of their higher-metallicity contaminants still have [Fe/H] < −2 (Youakim et al 2017). Finally, LSST is predicted to find tens of thousands of RR Lyrae stars throughout the Local Group (Oluseyi et al 2012); these span a wide range of metallicities but are all relatively old ( 10 Gyr).…”

Toward a Better Understanding of the Distance Scale From Rr Lyrae Variable Stars: A Case Study for the Inner Halo Globular Cluster NGC 6723

“…Since the LSST, for example, can detect RRLs in the local group of galaxies out to ≈1 Mpc scale over a time span of several years (Oluseyi et al 2012) and the future 30 m to 40 m class telescopes are expected to detect RRLs in nearby groups of galaxies, the utility of RRLs as a primary distance indicator will become more important. A more thorough study of the relation between |μ − μ r | and log ρ c d 2 with an extended sample of GCs or high-density regions would be desirable in the future because, if real, this relation could play a significant role in mitigating Majaess et al (2012) and NGC 6723 from our current study, where ρ c is the central luminosity density in units of solar luminosities per cubic parsec, d is the distance from the Sun in kiloparsecs, andμ − μ r is defined as the distance spread between the average computed using all RRLs and that using only those near the periphery (Majaess et al 2012) and measures the photometric contamination of GCs.…”

A Framework for Telescope Schedulers: With Applications to the Large Synoptic Survey Telescope

“…WFD 9 f / ∈ {y, u} visits with u filter and y filter is not allowed. NES objective functions can be defined based on LSST performance studies such as (Grav et al 2016), (Graham et al 2018), (Jacklin et al 2017), and (Oluseyi et al 2012)). The choice of optimization algorithm depends on the nature of the mission objective.…”